Hyperparameter optimization approaches to improve the performance of machine learning models for cardiovascular risk prediction.

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Title: Hyperparameter optimization approaches to improve the performance of machine learning models for cardiovascular risk prediction.
Authors: Sánchez-Jiménez, Eduardo1, Cuevas-Chávez, Alejandra1, Hernández, Yasmín1, yasmin.hp@cenidet.tecnm.mx, Ortiz-Hernandez, Javier1, Hernández-Aguilar, José Alberto2, Martínez-Rebollar, Alicia1, Estrada-Esquivel, Hugo1
Source: Journal of Intelligent & Fuzzy Systems; Feb2026, Vol. 50 Issue 2, p584-596, 13p
Database: Applied Science & Technology Source
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DbLabel: Applied Science & Technology Source
An: 192308545
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  Data: Hyperparameter optimization approaches to improve the performance of machine learning models for cardiovascular risk prediction.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Intelligent+%26+Fuzzy+Systems%22">Journal of Intelligent & Fuzzy Systems</searchLink>; Feb2026, Vol. 50 Issue 2, p584-596, 13p
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.3233/JIFS-219376
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      – Code: eng
        Text: English
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        PageCount: 13
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      – TitleFull: Hyperparameter optimization approaches to improve the performance of machine learning models for cardiovascular risk prediction.
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            NameFull: Sánchez-Jiménez, Eduardo
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            NameFull: Hernández-Aguilar, José Alberto
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              M: 02
              Text: Feb2026
              Type: published
              Y: 2026
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            – TitleFull: Journal of Intelligent & Fuzzy Systems
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